Uterine fibroids affect 77% of women by menopause in the U.S. and account for $2.1 billion in healthcare costs each year. Until recently, tumor tissu and cell culture studies investigating fibroid growth have been the primary sources for understanding fibroid pathophysiology. Genetic analysis provides a powerful and cost effective tool to identify etiological and causal factors, especially since a genetic predisposition to fibrods has already been shown from twin studies. As much as 69% of risk is explained by genetic factors. Racial disparities also support a role for genetics in fibroid risk. African American (AA) women have earlier age-of-onset, more numerous and larger fibroids with a greater lifetime incidence compared to European Americans (EAs). Among existing genetic analysis approaches, whole exome genotyping (WEG) is the most cost-effective and efficient compared to genome-wide association studies (GWAS) that focuses on common variants that may themselves not be causal. In this study, we will take advantage of a unique Vanderbilt resource, the BioVU DNA databank. BioVU has over 141,221 adult DNA samples linked to electronic medical records (EMR). From BioVU we have identified 3,535 AA subjects (612 cases and 2,923 controls) who meet our stringent inclusion criteria. Studies have shown that many women with fibroids are asymptomatic and without imaging as many as 51% of women may be misclassified. As a result, we have required pelvic imaging for both cases and controls. 50,000 BioVU subjects are currently being genotyped using Illumina's Exome Chip as part of an institutional initiative, including all AAs (completion summer of 2013). Our first aim is to conduc a WEG study of fibroids using AA BioVU subjects (n = 3,535) using logistic regression (common variants) and gene-based allele collapsing approaches (rare variants) to evaluate associations of SNPs with fibroids risk. Our second aim is to resequence exomes at 50X using AA BioVU extreme fibroid cases (n = 75) and controls (n = 75) to discover novel coding variants associated with fibroid risk. Extreme cases are those with youngest fibroid onset and largest number and size of fibroid. Extreme controls are the oldest subjects with no recorded history of fibroids. Aim 2 will allow us to validate variants discovered in Aim 1, as well as to identify nove variants not included on the genotyping chip. The NIH, NICHD, AHRQ, and the OWHR have made understanding the mechanisms underlying fibroid risk research priorities. This study is the largest and first whole exome experiment of fibroid among AAs. Immediate availability of samples and resources allow us to accomplish these R01 scope Aims within the budgetary and time constraints of an R03. These aims will allow us to identify novel variants associated with fibroids using two distinct approaches (WEG and exome resequencing using extreme phenotypes) and will lay the ground work for a future R01 examining rare variants associated with fibroids across racial groups. Our proposed study will fundamentally change knowledge about fibroids and lead to breakthroughs in understanding mechanisms in fibroid formation.